The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
| Version: | 0.1.0 |
| Imports: | randomForest, data.table, stats, rlist |
| Suggests: | foreach, knitr, rmarkdown, correlbinom |
| Published: | 2020-03-26 |
| Author: | Samir Rachid Zaim [aut, cre] |
| Maintainer: | Samir Rachid Zaim <samirrachidzaim at math.arizona.edu> |
| License: | GPL-2 |
| URL: | https://www.biorxiv.org/content/10.1101/681973v1.abstract |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | binomialRF results |
| Reference manual: | binomialRF.pdf |
| Vignettes: |
"binomialRF Feature Selection Vignette" |
| Package source: | binomialRF_0.1.0.tar.gz |
| Windows binaries: | r-devel: binomialRF_0.1.0.zip, r-release: binomialRF_0.1.0.zip, r-oldrel: binomialRF_0.1.0.zip |
| macOS binaries: | r-release: binomialRF_0.1.0.tgz, r-oldrel: binomialRF_0.1.0.tgz |
| Old sources: | binomialRF archive |
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